The object reconstruction is typically based on estimating the 3-D texture of an object (i.e., 3-D map). Various techniques have been developed in this field.
One of the approaches deals with triangulation [1] utilizing two cameras observing the same object. A relative shift of the same items in the images acquired by the cameras, respectively, is related to distance to these items. This technique is similar to 3-D estimation in human vision system. The main disadvantage of this approach is its low 3-D resolution, which strongly depends on a number of pixels in both cameras, the detail (e.g. texture) present on the scene, and a relative orientation of the cameras (angle and distance between them). Moreover, this approach does not provide for real-time mapping of objects because the extraction of 3-D information requires high level processing operations such as classification and registration. An additional problem with this approach is thatb 3D information obtained usually contains only relatively sparse samples of object depth.
Another known technique of the kind specified utilizes numerical algorithms based on the use of shadows of edges in the single captured image in order to compute the 3-D map of an object [2-6]. This technique, however, requires high level processing, and is inaccurate since the shadow of edges is the noisiest region of the image. Furthermore, this approach accumulates errors, since the first shadow is sued as a reference for the computation in the entire image.
Yet another approach for 3-D estimation is based on projection of patterns. Some techniques based on this approach utilize projection of a line onto an object and scanning the object with this line. A curvature generated in the line image is indicative of the 3-D map of the object. This technique, however, does not provide a real time process of the object reconstruction; it takes time to scan the object with the line; and the estimation becomes more distorted in case the object moves.
Some other techniques of this type utilize projection of special codes [7-9]. The code variation in an acquired image allows for computing the 3-D map of the object. These techniques are also not real-time since several projections are required. In addition, in order to obtain good 3-D resolution very large and complicated codes are required; this makes a projection system very expensive and not practical.
Yet other techniques based on the projection of patterns include single projection of a 2-D periodic pattern [10-14]. In this case, 3-D details of the object shift the lines of the periodic pattern in the captured image. A relative shift of these lines is related to the 3-D information of the object. Although with these techniques scanning is not required and the 3-D information can be obtained in real time, these technique suffer from the fact that 3-D information is wrapped since relative movements larger than the period of the projected pattern cannot be distinguished, and thus one cannot identify whether the obtained shift is that to be taken as is or an integer multiplied by the period size is to be added. Another disadvantage of this approach is associated with the depth of focus of the projected pattern. After a certain distance, the pattern is defocused, and it is very hard to extract numerically the relative shift of the periods.